Bayesian Identification of HLA Allotypes from Gibbs Sampling Motifs
This report presents the results of a Bayesian analysis comparing a Gibbs-sampled motif from dataset Unknown Dataset against a reference database of HLA allotype motifs. The analysis identified HLA_A0101 as the most probable match with high confidence. This suggests that the input peptide data likely represents binding specificities consistent with this HLA allotype.
This visualization compares the Position-Specific Scoring Matrix (PSSM) of the top-matched HLA allotype (HLA_A0101) with the Gibbs-derived motif. The comparison includes log-odds scores, information content, positional Gini coefficients, and similarity metrics.
This visualization shows the Bayesian analysis workflow including prior probabilities, likelihood values based on PSSM similarity, and resulting posterior probabilities for each HLA allotype. The top panel shows the flow of Bayesian analysis, while the bar charts show the comparative values for the top allotype matches.
This interactive network visualization shows the relationships between the Gibbs-derived motif and HLA allotypes. Node sizes represent posterior probabilities, with larger nodes indicating stronger matches. Edges represent the strength of the connection between the Gibbs motif and each allotype. Colors represent different HLA classes.
| Rank | Allotype | Prior | Likelihood | Posterior | Bayes Factor | P-value | Adjusted P | Significant |
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Position-Specific Scoring Matrices (PSSMs) capture amino acid preferences at each position of HLA binding motifs. We compare Gibbs-derived motifs with reference HLA allotype motifs using information theory metrics.
We apply Bayesian statistics to calculate the probability that a given Gibbs motif represents each HLA allotype, incorporating prior population frequencies and likelihood based on motif similarity.
We assess statistical significance through permutation testing and correct for multiple comparisons using the Benjamini-Hochberg procedure to control false discovery rate.